Volatility forecasting using time series data mining and evolutionary computation techniques

The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting ...

Several studies have applied genetic programming (GP) to the task of forecasting with favorable results. However, these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world time ...

Genetic Programming (GP) has proved its applicability for time series forecasting in a number of studies. The Dynamic Forecasting Genetic Program (DyFor GP) model builds on the GP technique by adding features that are tailored for the forecasting of ...